Robotic Expert System for Energy Management in Distributed Grid Ecosystem

2022 ◽  
pp. 736-763
Author(s):  
Ononiwu Gordon Chiagozie ◽  
Kennedy Chinedu Okafor ◽  
Nwaokolo F I
Keyword(s):  
Expert System ◽  
Energy Management ◽  
Energy Demand ◽  
Dimensional Space ◽  
Total Power ◽  
Support Vector ◽  
Self Healing ◽  
Power Demand ◽  
Look Ahead ◽  
Micro Grid

A robotic expert system (RES) for energy management (EM) in community-based micro-grids is developed using a fuzzy computational scheme. Within the micro-grid multi-dimensional space, embedded algorithms for residential homes, sectors and central controller units are introduced to perform EM in a collaborative manner. Demand response and load shedding are carried out within the community micro-grid to ascertain the behavioral responses based on changes in power demand levels. Various tests are carried out with an observable low error margin. It was observed that the system reduced the total power demand on the micro-grid by 20% of the total distributed power. Micro-grid RES, neuro-fuzzy control (NFC), and support vector regression (SVR) evaluations are compared considering the home units at 40kW of the generated capacity. The results gave a 35.79%, 31.58% and 32.63% energy demand, respectively. Consequently, RES provides a grid look-ahead prediction, annotated-self healing, and stability restoration.

Robotic Expert System for Energy Management in Distributed Grid Ecosystem

2020 ◽  
Vol 9 (1) ◽  
pp. 1-26
Author(s):  
Ononiwu Gordon Chiagozie ◽  
Kennedy Chinedu Okafor ◽  
Nwaokolo F I
Keyword(s):  
Expert System ◽  
Energy Management ◽  
Energy Demand ◽  
Dimensional Space ◽  
Total Power ◽  
Support Vector ◽  
Self Healing ◽  
Power Demand ◽  
Look Ahead ◽  
Micro Grid

A robotic expert system (RES) for energy management (EM) in community-based micro-grids is developed using a fuzzy computational scheme. Within the micro-grid multi-dimensional space, embedded algorithms for residential homes, sectors and central controller units are introduced to perform EM in a collaborative manner. Demand response and load shedding are carried out within the community micro-grid to ascertain the behavioral responses based on changes in power demand levels. Various tests are carried out with an observable low error margin. It was observed that the system reduced the total power demand on the micro-grid by 20% of the total distributed power. Micro-grid RES, neuro-fuzzy control (NFC), and support vector regression (SVR) evaluations are compared considering the home units at 40kW of the generated capacity. The results gave a 35.79%, 31.58% and 32.63% energy demand, respectively. Consequently, RES provides a grid look-ahead prediction, annotated-self healing, and stability restoration.

scholarly journals Short-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modeling

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2485 ◽  
Author(s):  
José-Luis Casteleiro-Roca ◽  
José Gómez-González ◽  
José Calvo-Rolle ◽  
Esteban Jove ◽  
Héctor Quintián ◽  
...  
Keyword(s):  
Energy Management ◽  
Energy Demand ◽  
Management Systems ◽  
Support Vector ◽  
Demand Forecast ◽  
Intelligent Modeling ◽  
Regression Methods ◽  
Energy Management Systems ◽  
Demand Information ◽  
Term Energy

The hotel industry is an important energy consumer that needs efficient energy management methods to guarantee its performance and sustainability. The new role of hotels as prosumers increases the difficulty in the design of these methods. Also, the scenery is more complex as renewable energy systems are present in the hotel energy mix. The performance of energy management systems greatly depends on the use of reliable predictions for energy load. This paper presents a new methodology to predict energy load in a hotel based on intelligent techniques. The model proposed is based on a hybrid intelligent topology implemented with a combination of clustering techniques and intelligent regression methods (Artificial Neural Network and Support Vector Regression). The model includes its own energy demand information, occupancy rate, and temperature as inputs. The validation was done using real hotel data and compared with time-series models. Forecasts obtained were satisfactory, showing a promising potential for its use in energy management systems in hotel resorts.

scholarly journals Optimal Energy Management Strategy for a DC Linked Hydro–PV–Wind Renewable Energy System for Hydroelectric Power Generation Optimization

2021 ◽  
Vol 1 (3) ◽  
pp. 9-18
Author(s):  
Adel Elgammal ◽  
Curtis Boodoo
Keyword(s):  
Energy Management ◽  
Energy Demand ◽  
Management Strategy ◽  
Power Plants ◽  
Energy System ◽  
Management Approach ◽  
Power Demand ◽  
Hydroelectric Power ◽  
Diesel Generator ◽  
Energy Management Strategy

The goal of this article is to create an intelligent energy management system that will control the stand-alone microgrid and power flow of a grid associated that includes Battery Energy Storage System, Fuel Cell, Wind Turbine, Diesel Generator, Photovoltaic, and a Hydro Power Plant. Storage systems are required for high dependability, while control systems are required for the system's optimum and steady functioning. The control, operation, and planning of both energy demand and production are all part of energy management. By controlling unpredictable power and providing an appropriate control algorithm for the entire system, the suggested energy management strategy is designed to handle diverse variations in power demand and supply. Under the TOU Tariff, the problem is presented as a discrete time multi-objective optimization method to minimize grid imported energy costs. It also maximizes earnings from surplus RE sales to the grid at a pre-determined RE feed-in tariff. Simulations were run using SIMULINK/MATLAB to validate and evaluate the suggested energy management approach under various power demand and power supply scenarios. The simulations indicate that the proposed energy management can fulfill demand at all times utilizing unreliable renewables like wind, solar, and hydroelectric power plants, as well as hydrogen fuel cells and batteries, without affecting load supply or power quality.

scholarly journals Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7191
Author(s):  
Samee Ullah Khan ◽  
Ijaz Ul Haq ◽  
Zulfiqar Ahmad Khan ◽  
Noman Khan ◽  
Mi Young Lee ◽  
...  
Keyword(s):  
Energy Management ◽  
Energy Demand ◽  
Power Demand ◽  
Smart Meters ◽  
Demand And Supply ◽  
Learning Framework ◽  
Proposed Model ◽  
Term Energy ◽  
Optimal Energy Management ◽  
Gated Recurrent Units

Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research. However, energy demand is increasing day by day in many countries due to rapid growth of the research domain population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching power demand with supply for optimal energy management. To resolve this issue, we present an intelligent deep learning framework that integrates Atrous Convolutional Layers (ACL) with Residual Gated Recurrent Units (RGRU) to establish balance between the demand and supply. Moreover, it accurately predicts short-term energy and delivers a systematic method of communication between consumers and energy distributors as well. To cope with the varying nature of electricity data, first data acquisition step is performed where data are collected from various sources such as smart meters and solar plants. In the second step a pre-processing method is applied on raw data to normalize and clean the data. Next, the refined data are passed to ACL for spatial feature extraction. Finally, a sequential learning model RGRU is used that learn from complicated patterns for the final output. The proposed model obtains the smallest values of Mean Square Error (MSE) including 0.1753, 0.0001, 0.0177 over IHEPC, KCB, and Solar datasets, respectively, which manifests better performance as compared to existing approaches.

scholarly journals Methodology for Estimating the Spatial and Temporal Power Demand of Private Electric Vehicles for an Entire Urban Region Using Open Data

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2081
Author(s):  
Florian Straub ◽  
Simon Streppel ◽  
Dietmar Göhlich
Keyword(s):  
Electric Vehicles ◽  
Energy Demand ◽  
Open Data ◽  
Temporal Distribution ◽  
Electrical Energy ◽  
Mobility Model ◽  
Total Power ◽  
Urban Region ◽  
Power Demand ◽  
Electrical Grid

With continuous proliferation of private battery electric vehicles (BEVs) in urban regions, the demand for electrical energy and power is constantly increasing. Electrical grid infrastructure operators are facing the question of where and to what extent they need to expand their infrastructure in order to meet the additional demand. Therefore, the aim of this paper is to develop an activity-based mobility model that supports electrical grid operators in detecting and evaluating possible overloads within the electrical grid, deriving from the aforementioned electrification. We apply our model, which fully relies on open data, to the urban area of Berlin. In addition to a household travel survey, statistics on the population density, the degree of motorisation, and the household income in fine spatial resolution are key data sources for generation of the model. The results show that the spatial distribution of the BEV charging energy demand is highly heterogeneous. The demand per capita is higher in peripheral areas of the city, while the demand per m2 area is higher in the inner city. For reference areas, we analysed the temporal distribution of the BEV charging power demand, by assuming that the vehicles are solely charged at their residential district. We show that the households’ power demand peak in the evening coincide with the BEV power demand peak while the total power demand can increase up to 77.9%.

A Comprehensive Review on Energy Management in Micro‐Grid System

2021 ◽  
pp. 1-24
Author(s):  
Sanjay Kumar ◽  
R. K. Saket ◽  
P. Sanjeevikumar ◽  
Jens Bo Holm‐Nielsen
Keyword(s):  
Energy Management ◽  
Grid System ◽  
Comprehensive Review ◽  
Micro Grid

scholarly journals Multi-Objective Energy Management of a Micro-Grid Considering Stochastic Nature of Load and Renewable Energy Resources

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 403
Author(s):  
Deyaa Ahmed ◽  
Mohamed Ebeed ◽  
Abdelfatah Ali ◽  
Ali S. Alghamdi ◽  
Salah Kamel
Keyword(s):  
Renewable Energy ◽  
Objective Function ◽  
Energy Management ◽  
Energy Resources ◽  
Management Problem ◽  
Renewable Energy Resources ◽  
Multi Objective ◽  
Stochastic Nature ◽  
Whale Optimization ◽  
Micro Grid

Optimal inclusion of a photovoltaic system and wind energy resources in electrical grids is a strenuous task due to the continuous variation of their output powers and stochastic nature. Thus, it is mandatory to consider the variations of the Renewable energy resources (RERs) for efficient energy management in the electric system. The aim of the paper is to solve the energy management of a micro-grid (MG) connected to the main power system considering the variations of load demand, photovoltaic (PV), and wind turbine (WT) under deterministic and probabilistic conditions. The energy management problem is solved using an efficient algorithm, namely equilibrium optimizer (EO), for a multi-objective function which includes cost minimization, voltage profile improvement, and voltage stability improvement. The simulation results reveal that the optimal installation of a grid-connected PV unit and WT can considerably reduce the total cost and enhance system performance. In addition to that, EO is superior to both whale optimization algorithm (WOA) and sine cosine algorithm (SCA) in terms of the reported objective function.

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